Last updated: 2024-08-27
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Knit directory: CART_TGFb/
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| Rmd | 9015511 | heinin | 2024-08-26 | comparative analysis and TGFb expression in scRNAseq |
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library(workflowr)
library(Seurat)
library(googlesheets4)
library(dplyr)
library(tidyverse)
library(ggrepel)
library(patchwork)
library(scProportionTest)
library(escape)
source("/home/hnatri/CART_TGFb/code/CART_plot_functions.R")
source("/home/hnatri/CART_TGFb/code/13384_tumor_ms_themes.R")
source("/home/hnatri/CART_TGFb/code/utilities.R")
setwd("/home/hnatri/CART_TGFb/")
set.seed(1234)
options(future.globals.maxSize = 30000 * 1024^2)
reduction <- "umap"
immune_seurat <- readRDS("/tgen_labs/banovich/BCTCSF/Heini/13384_Tumor/immune_reclustered_TGFb.rds")
tumor_seurat <- readRDS("/tgen_labs/banovich/BCTCSF/Heini/13384_Tumor/tumor_reclustered_TGFb.rds")
# Adding skull cluster numbers
immune_robyn <- readRDS("/scratch/hnatri/CART/fromRobyn/IMMUNEminus618_TGFbELISA+TGFbELISA2.rds")
DimPlot(immune_robyn, group.by = "seurat_clusters", label = T)
| Version | Author | Date |
|---|---|---|
| 9015511 | heinin | 2024-08-26 |
length(intersect(colnames(immune_seurat), colnames(immune_robyn)))
[1] 25098
length(setdiff(colnames(immune_seurat), colnames(immune_robyn)))
[1] 504
head(immune_robyn@meta.data)
orig.ident nCount_RNA nFeature_RNA percent.mt
Batch1_ACGCAGCAGTCATCCA-1 SeuratProject 10686 2552 2.227213
Batch1_ACTGAGTGTTCCGGCA-1 SeuratProject 4613 1652 2.124431
Batch1_AGCATACGTAGCTAAA-1 SeuratProject 7305 2086 3.449692
Batch1_ATGTGTGCAATAGCGG-1 SeuratProject 3776 1533 4.555085
Batch1_CAGTCCTAGCTTTGGT-1 SeuratProject 2719 1457 9.157779
Batch1_CGATTGATCTATCCCG-1 SeuratProject 9146 2694 4.089219
nCount_Protein nFeature_Protein
Batch1_ACGCAGCAGTCATCCA-1 4689 188
Batch1_ACTGAGTGTTCCGGCA-1 3599 187
Batch1_AGCATACGTAGCTAAA-1 3095 179
Batch1_ATGTGTGCAATAGCGG-1 1557 180
Batch1_CAGTCCTAGCTTTGGT-1 4541 191
Batch1_CGATTGATCTATCCCG-1 4598 191
Exome_Sample_Name Demultiplex_Assignment UPN
Batch1_ACGCAGCAGTCATCCA-1 BCTCSF_0058_1_PB_MNC_C1 SNG 243
Batch1_ACTGAGTGTTCCGGCA-1 BCTCSF_0058_1_PB_MNC_C1 SNG 243
Batch1_AGCATACGTAGCTAAA-1 BCTCSF_0058_1_PB_MNC_C1 SNG 243
Batch1_ATGTGTGCAATAGCGG-1 BCTCSF_0058_1_PB_MNC_C1 SNG 243
Batch1_CAGTCCTAGCTTTGGT-1 BCTCSF_0058_1_PB_MNC_C1 SNG 243
Batch1_CGATTGATCTATCCCG-1 BCTCSF_0058_1_PB_MNC_C1 SNG 243
Sample_Type Cycle Day Manufacture Cycle_Day
Batch1_ACGCAGCAGTCATCCA-1 FDT NA NA Tumor CycleNA_DayNA
Batch1_ACTGAGTGTTCCGGCA-1 FDT NA NA Tumor CycleNA_DayNA
Batch1_AGCATACGTAGCTAAA-1 FDT NA NA Tumor CycleNA_DayNA
Batch1_ATGTGTGCAATAGCGG-1 FDT NA NA Tumor CycleNA_DayNA
Batch1_CAGTCCTAGCTTTGGT-1 FDT NA NA Tumor CycleNA_DayNA
Batch1_CGATTGATCTATCCCG-1 FDT NA NA Tumor CycleNA_DayNA
Batch nCount_SCT nFeature_SCT S.Score
Batch1_ACGCAGCAGTCATCCA-1 Batch1 3133 1091 0.02190489
Batch1_ACTGAGTGTTCCGGCA-1 Batch1 3516 1647 -0.01952535
Batch1_AGCATACGTAGCTAAA-1 Batch1 3639 1679 -0.04382463
Batch1_ATGTGTGCAATAGCGG-1 Batch1 3385 1533 -0.02855645
Batch1_CAGTCCTAGCTTTGGT-1 Batch1 2821 1457 -0.09747976
Batch1_CGATTGATCTATCCCG-1 Batch1 3526 1709 -0.06602777
G2M.Score Phase IRB Sample_ID nCount_SoupX_RNA
Batch1_ACGCAGCAGTCATCCA-1 0.008389157 S 13384 243_Batch1 0
Batch1_ACTGAGTGTTCCGGCA-1 -0.031608446 G1 13384 243_Batch1 0
Batch1_AGCATACGTAGCTAAA-1 0.036195260 G2M 13384 243_Batch1 0
Batch1_ATGTGTGCAATAGCGG-1 -0.108682135 G1 13384 243_Batch1 0
Batch1_CAGTCCTAGCTTTGGT-1 0.045369327 G2M 13384 243_Batch1 0
Batch1_CGATTGATCTATCCCG-1 -0.012711941 G1 13384 243_Batch1 0
nFeature_SoupX_RNA percent.mt_RNA percent.ribo_RNA
Batch1_ACGCAGCAGTCATCCA-1 0 NA NA
Batch1_ACTGAGTGTTCCGGCA-1 0 NA NA
Batch1_AGCATACGTAGCTAAA-1 0 NA NA
Batch1_ATGTGTGCAATAGCGG-1 0 NA NA
Batch1_CAGTCCTAGCTTTGGT-1 0 NA NA
Batch1_CGATTGATCTATCCCG-1 0 NA NA
percent.mt_SoupX_RNA percent.ribo_SoupX_RNA
Batch1_ACGCAGCAGTCATCCA-1 NA NA
Batch1_ACTGAGTGTTCCGGCA-1 NA NA
Batch1_AGCATACGTAGCTAAA-1 NA NA
Batch1_ATGTGTGCAATAGCGG-1 NA NA
Batch1_CAGTCCTAGCTTTGGT-1 NA NA
Batch1_CGATTGATCTATCCCG-1 NA NA
nCount_Hash nFeature_Hash Hash_maxID Hash_secondID
Batch1_ACGCAGCAGTCATCCA-1 0 0 <NA> <NA>
Batch1_ACTGAGTGTTCCGGCA-1 0 0 <NA> <NA>
Batch1_AGCATACGTAGCTAAA-1 0 0 <NA> <NA>
Batch1_ATGTGTGCAATAGCGG-1 0 0 <NA> <NA>
Batch1_CAGTCCTAGCTTTGGT-1 0 0 <NA> <NA>
Batch1_CGATTGATCTATCCCG-1 0 0 <NA> <NA>
Hash_margin Hash_classification
Batch1_ACGCAGCAGTCATCCA-1 NA <NA>
Batch1_ACTGAGTGTTCCGGCA-1 NA <NA>
Batch1_AGCATACGTAGCTAAA-1 NA <NA>
Batch1_ATGTGTGCAATAGCGG-1 NA <NA>
Batch1_CAGTCCTAGCTTTGGT-1 NA <NA>
Batch1_CGATTGATCTATCCCG-1 NA <NA>
Hash_classification.global hash.ID FID_GEXFB
Batch1_ACGCAGCAGTCATCCA-1 <NA> <NA> <NA>
Batch1_ACTGAGTGTTCCGGCA-1 <NA> <NA> <NA>
Batch1_AGCATACGTAGCTAAA-1 <NA> <NA> <NA>
Batch1_ATGTGTGCAATAGCGG-1 <NA> <NA> <NA>
Batch1_CAGTCCTAGCTTTGGT-1 <NA> <NA> <NA>
Batch1_CGATTGATCTATCCCG-1 <NA> <NA> <NA>
seurat_clusters IFNg_score_SER IFNg_score_TF
Batch1_ACGCAGCAGTCATCCA-1 10 2.64759961886982 <NA>
Batch1_ACTGAGTGTTCCGGCA-1 1 2.64759961886982 <NA>
Batch1_AGCATACGTAGCTAAA-1 5 2.64759961886982 <NA>
Batch1_ATGTGTGCAATAGCGG-1 9 2.64759961886982 <NA>
Batch1_CAGTCCTAGCTTTGGT-1 4 2.64759961886982 <NA>
Batch1_CGATTGATCTATCCCG-1 10 2.64759961886982 <NA>
IFNg_score_CSF Survival.time.in.Months.from.surgery
Batch1_ACGCAGCAGTCATCCA-1 <NA> NA
Batch1_ACTGAGTGTTCCGGCA-1 <NA> NA
Batch1_AGCATACGTAGCTAAA-1 <NA> NA
Batch1_ATGTGTGCAATAGCGG-1 <NA> NA
Batch1_CAGTCCTAGCTTTGGT-1 <NA> NA
Batch1_CGATTGATCTATCCCG-1 <NA> NA
Death.Status DS Grade Diagnosis.Histology
Batch1_ACGCAGCAGTCATCCA-1 NA NA <NA> Glioblastoma, NOS
Batch1_ACTGAGTGTTCCGGCA-1 NA NA <NA> Glioblastoma, NOS
Batch1_AGCATACGTAGCTAAA-1 NA NA <NA> Glioblastoma, NOS
Batch1_ATGTGTGCAATAGCGG-1 NA NA <NA> Glioblastoma, NOS
Batch1_CAGTCCTAGCTTTGGT-1 NA NA <NA> Glioblastoma, NOS
Batch1_CGATTGATCTATCCCG-1 NA NA <NA> Glioblastoma, NOS
PriorAvastin No.Prior.Chemo No.Prior.Brain.Surgeries
Batch1_ACGCAGCAGTCATCCA-1 yes 3 2
Batch1_ACTGAGTGTTCCGGCA-1 yes 3 2
Batch1_AGCATACGTAGCTAAA-1 yes 3 2
Batch1_ATGTGTGCAATAGCGG-1 yes 3 2
Batch1_CAGTCCTAGCTTTGGT-1 yes 3 2
Batch1_CGATTGATCTATCCCG-1 yes 3 2
No.Prior.Radiations MRPplus.Overall.Best.Response
Batch1_ACGCAGCAGTCATCCA-1 2 Progression Disease (PD)
Batch1_ACTGAGTGTTCCGGCA-1 2 Progression Disease (PD)
Batch1_AGCATACGTAGCTAAA-1 2 Progression Disease (PD)
Batch1_ATGTGTGCAATAGCGG-1 2 Progression Disease (PD)
Batch1_CAGTCCTAGCTTTGGT-1 2 Progression Disease (PD)
Batch1_CGATTGATCTATCCCG-1 2 Progression Disease (PD)
binary_response CD3_score CD3_high Collection.Date
Batch1_ACGCAGCAGTCATCCA-1 PD 1 FALSE <NA>
Batch1_ACTGAGTGTTCCGGCA-1 PD 1 FALSE <NA>
Batch1_AGCATACGTAGCTAAA-1 PD 1 FALSE <NA>
Batch1_ATGTGTGCAATAGCGG-1 PD 1 FALSE <NA>
Batch1_CAGTCCTAGCTTTGGT-1 PD 1 FALSE <NA>
Batch1_CGATTGATCTATCCCG-1 PD 1 FALSE <NA>
EGFR.Amplification EGFRvIII..Exon.2.7.deletion
Batch1_ACGCAGCAGTCATCCA-1 <NA> <NA>
Batch1_ACTGAGTGTTCCGGCA-1 <NA> <NA>
Batch1_AGCATACGTAGCTAAA-1 <NA> <NA>
Batch1_ATGTGTGCAATAGCGG-1 <NA> <NA>
Batch1_CAGTCCTAGCTTTGGT-1 <NA> <NA>
Batch1_CGATTGATCTATCCCG-1 <NA> <NA>
EGFR.Missense TP53.Frameshift TP53.Missense
Batch1_ACGCAGCAGTCATCCA-1 <NA> <NA> <NA>
Batch1_ACTGAGTGTTCCGGCA-1 <NA> <NA> <NA>
Batch1_AGCATACGTAGCTAAA-1 <NA> <NA> <NA>
Batch1_ATGTGTGCAATAGCGG-1 <NA> <NA> <NA>
Batch1_CAGTCCTAGCTTTGGT-1 <NA> <NA> <NA>
Batch1_CGATTGATCTATCCCG-1 <NA> <NA> <NA>
TP53.Nonsense TERT.Promoter.Mutation IDH1.Missense
Batch1_ACGCAGCAGTCATCCA-1 <NA> <NA> <NA>
Batch1_ACTGAGTGTTCCGGCA-1 <NA> <NA> <NA>
Batch1_AGCATACGTAGCTAAA-1 <NA> <NA> <NA>
Batch1_ATGTGTGCAATAGCGG-1 <NA> <NA> <NA>
Batch1_CAGTCCTAGCTTTGGT-1 <NA> <NA> <NA>
Batch1_CGATTGATCTATCCCG-1 <NA> <NA> <NA>
IDH2.Missense PTEN.Codon.Deletion PTEN.Frameshift
Batch1_ACGCAGCAGTCATCCA-1 <NA> <NA> <NA>
Batch1_ACTGAGTGTTCCGGCA-1 <NA> <NA> <NA>
Batch1_AGCATACGTAGCTAAA-1 <NA> <NA> <NA>
Batch1_ATGTGTGCAATAGCGG-1 <NA> <NA> <NA>
Batch1_CAGTCCTAGCTTTGGT-1 <NA> <NA> <NA>
Batch1_CGATTGATCTATCCCG-1 <NA> <NA> <NA>
PTEN.Interference.of.splice.acceptor.site.in.Intron.6
Batch1_ACGCAGCAGTCATCCA-1 <NA>
Batch1_ACTGAGTGTTCCGGCA-1 <NA>
Batch1_AGCATACGTAGCTAAA-1 <NA>
Batch1_ATGTGTGCAATAGCGG-1 <NA>
Batch1_CAGTCCTAGCTTTGGT-1 <NA>
Batch1_CGATTGATCTATCCCG-1 <NA>
CD3_high_low cluster celltype myeloid_cluster
Batch1_ACGCAGCAGTCATCCA-1 Low 0 Lymph1 <NA>
Batch1_ACTGAGTGTTCCGGCA-1 Low 0 Lymph1 <NA>
Batch1_AGCATACGTAGCTAAA-1 Low 0 Lymph1 <NA>
Batch1_ATGTGTGCAATAGCGG-1 Low 0 Lymph1 <NA>
Batch1_CAGTCCTAGCTTTGGT-1 Low 8 Lymph2 <NA>
Batch1_CGATTGATCTATCCCG-1 Low 0 Lymph1 <NA>
celltype2 nCount_integrated_sct
Batch1_ACGCAGCAGTCATCCA-1 Teff 0
Batch1_ACTGAGTGTTCCGGCA-1 Teff 0
Batch1_AGCATACGTAGCTAAA-1 Teff 0
Batch1_ATGTGTGCAATAGCGG-1 Teff 0
Batch1_CAGTCCTAGCTTTGGT-1 NK 0
Batch1_CGATTGATCTATCCCG-1 Teff 0
nFeature_integrated_sct TGFbELISA TGFbELISA2
Batch1_ACGCAGCAGTCATCCA-1 0 ND ND
Batch1_ACTGAGTGTTCCGGCA-1 0 ND ND
Batch1_AGCATACGTAGCTAAA-1 0 ND ND
Batch1_ATGTGTGCAATAGCGG-1 0 ND ND
Batch1_CAGTCCTAGCTTTGGT-1 0 ND ND
Batch1_CGATTGATCTATCCCG-1 0 ND ND
table(immune_robyn$seurat_clusters)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
4642 4509 3785 3260 2549 1569 0 1095 822 608 562 451 368 344 315 291
16 17 18
142 125 0
immune_robyn$seurat_clusters <- as.character(immune_robyn$seurat_clusters)
immune_seurat$skull_cluster <- mapvalues(x = colnames(immune_seurat),
from = colnames(immune_robyn),
to = immune_robyn$seurat_clusters)
head(table(immune_seurat$skull_cluster), n = 20)
0 1 10
4540 4497 560
11 12 13
443 360 340
14 15 16
313 290 135
17 2 3
118 3720 3195
4 42-1_AAGGAGCAGCACACAG-1 42-1_AAGGTTCGTAGAGGAA-1
2513 1 1
42-1_ACTGAGTCAATGGACG-1 42-1_AGCGTCGCAAGTTAAG-1 42-1_AGGTCATTCCTAAGTG-1
1 1 1
42-1_ATCCGAAGTCATGCAT-1 42-1_ATGCGATAGTACGCGA-1
1 1
table(immune_seurat$skull_cluster)["0"]
0
4540
immune_seurat$skull_cluster <- ifelse(colnames(immune_seurat) %in% colnames(immune_robyn), immune_seurat$skull_cluster, NA)
# Plotting cell type annotations
DimPlot(immune_seurat,
group.by = "celltype",
cols = immune_fibro_celltype_col,
reduction = reduction,
label = T,
label.box = T,
label.size = 3,
repel = T,
raster = T,
raster.dpi = c(1024, 1024),
pt.size = 3) +
ggtitle("") +
theme_classic() +
NoLegend() +
NoAxes() +
coord_fixed(1)
| Version | Author | Date |
|---|---|---|
| 9015511 | heinin | 2024-08-26 |
DimPlot(immune_seurat,
group.by = "skull_cluster",
#cols = immune_fibro_celltype_col,
reduction = reduction,
label = T,
label.box = T,
label.size = 3,
repel = T,
raster = T,
raster.dpi = c(1024, 1024),
pt.size = 3) +
ggtitle("") +
theme_classic() +
NoLegend() +
NoAxes() +
coord_fixed(1)
DimPlot(tumor_seurat,
group.by = "celltype",
cols = tumor_celltype_col,
reduction = "umap",
label = T,
label.box = T,
label.size = 3,
repel = T,
raster = T,
raster.dpi = c(1024, 1024),
pt.size = 3) +
ggtitle("") +
theme_classic() +
NoLegend() +
NoAxes() +
coord_fixed(1)
rownames(tumor_seurat)[grepl("^TGF", rownames(tumor_seurat))]
[1] "TGFBR3" "TGFB2" "TGFA" "TGFBRAP1" "TGFBR2" "TGFBI"
[7] "TGFBR1" "TGFB3" "TGFB1I1" "TGFBR3L" "TGFB1"
VlnPlot(tumor_seurat, group.by = "TGFbELISA2",
features = rownames(tumor_seurat)[grepl("^TGF", rownames(tumor_seurat))],
ncol = 4,
slot = "counts")
| Version | Author | Date |
|---|---|---|
| 9015511 | heinin | 2024-08-26 |
# Correlation between measurements and mRNA level
tgfb_elisa <- read.table("/home/hnatri/CART_TGFb/data/ELISA1_TGFb.tsv", sep = "\t", header = T)
tumor_seurat$log10TGFb <- mapvalues(x = tumor_seurat$UPN,
from = tgfb_elisa$UPN,
to = tgfb_elisa$log10TGFb)
layer_data_RNA <- LayerData(tumor_seurat,
assay = "RNA",
layer = "counts")
layer_data_RNA <- as.data.frame(t(as.matrix(layer_data_RNA)))
layer_data_RNA[1:10, 1:10]
OR4F5 OR4F29 OR4F16 SAMD11 NOC2L KLHL17 PLEKHN1 PERM1
Batch1_ACGGGTCTCCAGAGGA-1 0 0 0 0 0 0 0 0
Batch1_ACTTTCAGTCCGACGT-1 0 0 0 0 0 0 0 0
Batch1_AGGGTGACACCTGGTG-1 0 0 0 0 0 0 0 0
Batch1_CACACAAGTACCGTAT-1 0 0 0 0 0 0 0 0
Batch1_CCTTCCCCAAGAAGAG-1 0 0 0 0 1 0 0 0
Batch1_CGATGTACAGGAATCG-1 0 0 0 0 0 0 0 0
Batch1_GAACCTACACCGATAT-1 0 0 0 0 0 0 0 0
Batch1_GAATGAAGTCACCTAA-1 0 0 0 0 2 1 0 0
Batch1_GCAGCCAAGTGTCCCG-1 0 0 0 0 0 0 0 0
Batch1_GTCTTCGAGTCGTTTG-1 0 0 0 0 1 0 0 0
HES4 ISG15
Batch1_ACGGGTCTCCAGAGGA-1 0 0
Batch1_ACTTTCAGTCCGACGT-1 0 0
Batch1_AGGGTGACACCTGGTG-1 0 0
Batch1_CACACAAGTACCGTAT-1 0 0
Batch1_CCTTCCCCAAGAAGAG-1 1 0
Batch1_CGATGTACAGGAATCG-1 0 0
Batch1_GAACCTACACCGATAT-1 0 0
Batch1_GAATGAAGTCACCTAA-1 5 0
Batch1_GCAGCCAAGTGTCCCG-1 0 0
Batch1_GTCTTCGAGTCGTTTG-1 1 0
gene_list <- c(rownames(tumor_seurat)[grepl("^TGF", rownames(tumor_seurat))],
"SMAD2", "SMAD3", "SMAD4")
layer_data_RNA <- layer_data_RNA[, gene_list]
identical(rownames(layer_data_RNA), colnames(tumor_seurat))
[1] TRUE
layer_data_RNA$log10TGFb <- tumor_seurat$log10TGFb
layer_data_RNA$UPN <- tumor_seurat$UPN
layer_data_RNA$TGFbELISA2 <- tumor_seurat$TGFbELISA2
layer_data_RNA_filtered <- layer_data_RNA %>% filter(UPN %in% tgfb_elisa$UPN)
head(layer_data_RNA_filtered)
TGFBR3 TGFB2 TGFA TGFBRAP1 TGFBR2 TGFBI TGFBR1 TGFB3
Batch1_AACTTTCTCACCACCT-1 0 0 0 0 0 0 0 0
Batch1_AGATTGCGTAATTGGA-1 0 0 0 0 0 0 0 0
Batch1_AGCTCTCGTATGAATG-1 0 0 0 0 0 0 1 0
Batch1_ATTGGACGTAGTACCT-1 0 0 0 0 0 0 0 0
Batch1_CACCAGGCAATAGCAA-1 0 0 0 0 0 0 0 0
Batch1_CATCAGAGTCCAGTGC-1 0 0 0 0 0 0 0 0
TGFB1I1 TGFBR3L TGFB1 SMAD2 SMAD3 SMAD4 log10TGFb
Batch1_AACTTTCTCACCACCT-1 0 0 0 0 0 0 2.072273095
Batch1_AGATTGCGTAATTGGA-1 0 0 0 0 0 0 2.072273095
Batch1_AGCTCTCGTATGAATG-1 0 0 0 0 0 0 2.072273095
Batch1_ATTGGACGTAGTACCT-1 0 0 0 0 0 1 2.072273095
Batch1_CACCAGGCAATAGCAA-1 0 0 0 0 1 0 2.072273095
Batch1_CATCAGAGTCCAGTGC-1 0 0 1 1 0 0 2.072273095
UPN TGFbELISA2
Batch1_AACTTTCTCACCACCT-1 185 LowTGFb
Batch1_AGATTGCGTAATTGGA-1 185 LowTGFb
Batch1_AGCTCTCGTATGAATG-1 185 LowTGFb
Batch1_ATTGGACGTAGTACCT-1 185 LowTGFb
Batch1_CACCAGGCAATAGCAA-1 185 LowTGFb
Batch1_CATCAGAGTCCAGTGC-1 185 LowTGFb
layer_data_RNA_filtered %>% ggplot(aes(x = as.numeric(log10TGFb), y = TGFB2, color = TGFbELISA2)) +
geom_point() +
theme_bw()
layer_data_RNA_filtered %>% ggplot(aes(x = as.numeric(log10TGFb), y = log2(TGFB2), color = TGFbELISA2)) +
geom_point() +
theme_bw()
layer_data_RNA_filtered %>% ggplot(aes(x = as.numeric(log10TGFb), y = TGFBI, color = TGFbELISA2)) +
geom_point() +
theme_bw()
layer_data_RNA_filtered %>% ggplot(aes(x = as.numeric(log10TGFb), y = log2(TGFBI), color = TGFbELISA2)) +
geom_point() +
theme_bw()
DefaultAssay(immune_seurat) <- "RNA"
# Dropping MT and RP genes before calling markers
RBMTgenes <- grep(pattern = "^RP[SL]|^MRP[SL]|^MT-",
x = rownames(immune_seurat@assays$RNA@data),
value = TRUE, invert = TRUE)
immune_seurat <- subset(immune_seurat, features = RBMTgenes)
# Top markers for each cluster
markers <- presto::wilcoxauc(immune_seurat,
group_by = "celltype",
assay = "data",
seurat_assay = "RNA")
top_markers <- markers %>% group_by(group) %>% slice_max(order_by = auc, n = 2)
FeaturePlot(immune_seurat,
features = top_markers$feature,
ncol = 6,
reduction = "umap",
raster = T,
cols = c("gray89", "tomato3")) &
coord_fixed(ratio = 1) &
theme_bw() &
NoLegend() &
manuscript_theme
| Version | Author | Date |
|---|---|---|
| 9015511 | heinin | 2024-08-26 |
top_markers <- markers %>% group_by(group) %>% slice_max(order_by = auc, n = 5)
# seurat_object, plot_features, group_var, group_colors, column_title, km=5, row.order = NULL
create_dotplot_heatmap_horizontal(seurat_object = immune_seurat,
plot_features = unique(top_markers$feature),
group_var = "celltype",
group_colors = immune_fibro_celltype_col,
column_title = "",
km = 5,
col.order = NULL)
| Version | Author | Date |
|---|---|---|
| 9015511 | heinin | 2024-08-26 |
| Version | Author | Date |
|---|---|---|
| 9015511 | heinin | 2024-08-26 |
unique(immune_seurat$TGFbELISA2)
[1] "ND" "LowTGFb" "HighTGFb"
# For each cluster, top DEGs between TGFb high and low
DEG_TGFb <- lapply(unique(immune_seurat$celltype), function(xx){
data_subset <- subset(immune_seurat, subset = celltype == xx)
Idents(data_subset) <- data_subset$TGFbELISA2
if (all((c("HighTGFb", "LowTGFb") %in% data_subset$TGFbELISA2) == c(T, T))){
markers <- FindMarkers(data_subset,
ident.1 = "HighTGFb",
ident.2 = "LowTGFb",
assay = "RNA",
verbose = F)
markers$feature <- rownames(markers)
markers$celltype <- xx
return(markers)
} else {
return(NULL)
}
})
names(DEG_TGFb) <- unique(immune_seurat$celltype)
DEG_TGFb[sapply(DEG_TGFb, is.null)] <- NULL
DEG_TGFb_df <- as.data.frame(do.call(rbind, DEG_TGFb))
# Distribution of p-values and log2FC
hist(DEG_TGFb_df$p_val)
| Version | Author | Date |
|---|---|---|
| 9015511 | heinin | 2024-08-26 |
hist(DEG_TGFb_df$avg_log2FC)
| Version | Author | Date |
|---|---|---|
| 9015511 | heinin | 2024-08-26 |
DEG_TGFb_df_sig <- DEG_TGFb_df %>%
filter(p_val < 0.01,
abs(avg_log2FC) > 2,
(pct.1 > 0.50 | pct.2 > 0.50))
dim(DEG_TGFb_df_sig)
[1] 235 7
# Saving to a file
write.table(DEG_TGFb_df_sig,
"/scratch/hnatri/CART/TGFb_high_vs_low_DEGs_immune_sig.tsv",
sep = "\t", quote = F, row.names = F)
# Plotting
table(DEG_TGFb_df_sig$celltype) %>% as.data.frame() %>%
ggplot(aes(x = reorder(Var1, -Freq), y = Freq, fill = Var1)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = immune_fibro_celltype_col) +
theme_classic() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
NoLegend() +
xlab("Cell type") +
ylab("# DEGs, TGFb high vs. low")
# Adding HALLMARK pathways from another object
immune_fibro_hallmark <- readRDS("/tgen_labs/banovich/BCTCSF/Heini/13384_Tumor/13384_immune_fibro_GSEA_C2_H.rds")
colnames(immune_fibro_hallmark@meta.data)[grep("HALLMARK", colnames(immune_fibro_hallmark@meta.data))]
[1] "HALLMARK_ADIPOGENESIS"
[2] "HALLMARK_ALLOGRAFT_REJECTION"
[3] "HALLMARK_ANDROGEN_RESPONSE"
[4] "HALLMARK_ANGIOGENESIS"
[5] "HALLMARK_APICAL_JUNCTION"
[6] "HALLMARK_APICAL_SURFACE"
[7] "HALLMARK_APOPTOSIS"
[8] "HALLMARK_BILE_ACID_METABOLISM"
[9] "HALLMARK_CHOLESTEROL_HOMEOSTASIS"
[10] "HALLMARK_COAGULATION"
[11] "HALLMARK_COMPLEMENT"
[12] "HALLMARK_DNA_REPAIR"
[13] "HALLMARK_E2F_TARGETS"
[14] "HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION"
[15] "HALLMARK_ESTROGEN_RESPONSE_EARLY"
[16] "HALLMARK_ESTROGEN_RESPONSE_LATE"
[17] "HALLMARK_FATTY_ACID_METABOLISM"
[18] "HALLMARK_G2M_CHECKPOINT"
[19] "HALLMARK_GLYCOLYSIS"
[20] "HALLMARK_HEDGEHOG_SIGNALING"
[21] "HALLMARK_HEME_METABOLISM"
[22] "HALLMARK_HYPOXIA"
[23] "HALLMARK_IL2_STAT5_SIGNALING"
[24] "HALLMARK_IL6_JAK_STAT3_SIGNALING"
[25] "HALLMARK_INFLAMMATORY_RESPONSE"
[26] "HALLMARK_INTERFERON_ALPHA_RESPONSE"
[27] "HALLMARK_INTERFERON_GAMMA_RESPONSE"
[28] "HALLMARK_KRAS_SIGNALING_DN"
[29] "HALLMARK_KRAS_SIGNALING_UP"
[30] "HALLMARK_MITOTIC_SPINDLE"
[31] "HALLMARK_MTORC1_SIGNALING"
[32] "HALLMARK_MYC_TARGETS_V1"
[33] "HALLMARK_MYC_TARGETS_V2"
[34] "HALLMARK_MYOGENESIS"
[35] "HALLMARK_NOTCH_SIGNALING"
[36] "HALLMARK_OXIDATIVE_PHOSPHORYLATION"
[37] "HALLMARK_P53_PATHWAY"
[38] "HALLMARK_PANCREAS_BETA_CELLS"
[39] "HALLMARK_PEROXISOME"
[40] "HALLMARK_PI3K_AKT_MTOR_SIGNALING"
[41] "HALLMARK_PROTEIN_SECRETION"
[42] "HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY"
[43] "HALLMARK_SPERMATOGENESIS"
[44] "HALLMARK_TGF_BETA_SIGNALING"
[45] "HALLMARK_TNFA_SIGNALING_VIA_NFKB"
[46] "HALLMARK_UNFOLDED_PROTEIN_RESPONSE"
[47] "HALLMARK_UV_RESPONSE_DN"
[48] "HALLMARK_UV_RESPONSE_UP"
[49] "HALLMARK_WNT_BETA_CATENIN_SIGNALING"
[50] "HALLMARK_XENOBIOTIC_METABOLISM"
for(i in colnames(immune_fibro_hallmark@meta.data)[grep("HALLMARK", colnames(immune_fibro_hallmark@meta.data))]){
immune_seurat@meta.data[,i] <- mapvalues(x = rownames(immune_seurat@meta.data),
from = rownames(immune_fibro_hallmark@meta.data),
to = immune_fibro_hallmark@meta.data[,i])
}
rm(immune_fibro_hallmark)
tumors_hallmark <- readRDS("/tgen_labs/banovich/BCTCSF/Heini/13384_Tumor/13384_tumors_GSEA_C2_H.rds")
colnames(tumors_hallmark@meta.data)[grep("HALLMARK", colnames(tumors_hallmark@meta.data))]
[1] "HALLMARK_ADIPOGENESIS"
[2] "HALLMARK_ALLOGRAFT_REJECTION"
[3] "HALLMARK_ANDROGEN_RESPONSE"
[4] "HALLMARK_ANGIOGENESIS"
[5] "HALLMARK_APICAL_JUNCTION"
[6] "HALLMARK_APICAL_SURFACE"
[7] "HALLMARK_APOPTOSIS"
[8] "HALLMARK_BILE_ACID_METABOLISM"
[9] "HALLMARK_CHOLESTEROL_HOMEOSTASIS"
[10] "HALLMARK_COAGULATION"
[11] "HALLMARK_COMPLEMENT"
[12] "HALLMARK_DNA_REPAIR"
[13] "HALLMARK_E2F_TARGETS"
[14] "HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION"
[15] "HALLMARK_ESTROGEN_RESPONSE_EARLY"
[16] "HALLMARK_ESTROGEN_RESPONSE_LATE"
[17] "HALLMARK_FATTY_ACID_METABOLISM"
[18] "HALLMARK_G2M_CHECKPOINT"
[19] "HALLMARK_GLYCOLYSIS"
[20] "HALLMARK_HEDGEHOG_SIGNALING"
[21] "HALLMARK_HEME_METABOLISM"
[22] "HALLMARK_HYPOXIA"
[23] "HALLMARK_IL2_STAT5_SIGNALING"
[24] "HALLMARK_IL6_JAK_STAT3_SIGNALING"
[25] "HALLMARK_INFLAMMATORY_RESPONSE"
[26] "HALLMARK_INTERFERON_ALPHA_RESPONSE"
[27] "HALLMARK_INTERFERON_GAMMA_RESPONSE"
[28] "HALLMARK_KRAS_SIGNALING_DN"
[29] "HALLMARK_KRAS_SIGNALING_UP"
[30] "HALLMARK_MITOTIC_SPINDLE"
[31] "HALLMARK_MTORC1_SIGNALING"
[32] "HALLMARK_MYC_TARGETS_V1"
[33] "HALLMARK_MYC_TARGETS_V2"
[34] "HALLMARK_MYOGENESIS"
[35] "HALLMARK_NOTCH_SIGNALING"
[36] "HALLMARK_OXIDATIVE_PHOSPHORYLATION"
[37] "HALLMARK_P53_PATHWAY"
[38] "HALLMARK_PANCREAS_BETA_CELLS"
[39] "HALLMARK_PEROXISOME"
[40] "HALLMARK_PI3K_AKT_MTOR_SIGNALING"
[41] "HALLMARK_PROTEIN_SECRETION"
[42] "HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY"
[43] "HALLMARK_SPERMATOGENESIS"
[44] "HALLMARK_TGF_BETA_SIGNALING"
[45] "HALLMARK_TNFA_SIGNALING_VIA_NFKB"
[46] "HALLMARK_UNFOLDED_PROTEIN_RESPONSE"
[47] "HALLMARK_UV_RESPONSE_DN"
[48] "HALLMARK_UV_RESPONSE_UP"
[49] "HALLMARK_WNT_BETA_CATENIN_SIGNALING"
[50] "HALLMARK_XENOBIOTIC_METABOLISM"
for(i in colnames(tumors_hallmark@meta.data)[grep("HALLMARK", colnames(tumors_hallmark@meta.data))]){
tumor_seurat@meta.data[,i] <- mapvalues(x = rownames(tumor_seurat@meta.data),
from = rownames(tumors_hallmark@meta.data),
to = tumors_hallmark@meta.data[,i])
}
rm(tumors_hallmark)
#saveRDS(immune_seurat, "/tgen_labs/banovich/BCTCSF/Heini/13384_Tumor/13384_immune_GSEA_C2_H.rds")
#saveRDS(tumor_seurat, "/tgen_labs/banovich/BCTCSF/Heini/13384_Tumor/13384_tumor_GSEA_C2_H.rds")
immune_seurat <- readRDS("/tgen_labs/banovich/BCTCSF/Heini/13384_Tumor/13384_immune_GSEA_C2_H.rds")
tumor_seurat <- readRDS("/tgen_labs/banovich/BCTCSF/Heini/13384_Tumor/13384_tumor_GSEA_C2_H.rds")
colnames(immune_seurat@meta.data)[grep("HALLMARK", colnames(immune_seurat@meta.data))]
[1] "HALLMARK_ADIPOGENESIS"
[2] "HALLMARK_ALLOGRAFT_REJECTION"
[3] "HALLMARK_ANDROGEN_RESPONSE"
[4] "HALLMARK_ANGIOGENESIS"
[5] "HALLMARK_APICAL_JUNCTION"
[6] "HALLMARK_APICAL_SURFACE"
[7] "HALLMARK_APOPTOSIS"
[8] "HALLMARK_BILE_ACID_METABOLISM"
[9] "HALLMARK_CHOLESTEROL_HOMEOSTASIS"
[10] "HALLMARK_COAGULATION"
[11] "HALLMARK_COMPLEMENT"
[12] "HALLMARK_DNA_REPAIR"
[13] "HALLMARK_E2F_TARGETS"
[14] "HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION"
[15] "HALLMARK_ESTROGEN_RESPONSE_EARLY"
[16] "HALLMARK_ESTROGEN_RESPONSE_LATE"
[17] "HALLMARK_FATTY_ACID_METABOLISM"
[18] "HALLMARK_G2M_CHECKPOINT"
[19] "HALLMARK_GLYCOLYSIS"
[20] "HALLMARK_HEDGEHOG_SIGNALING"
[21] "HALLMARK_HEME_METABOLISM"
[22] "HALLMARK_HYPOXIA"
[23] "HALLMARK_IL2_STAT5_SIGNALING"
[24] "HALLMARK_IL6_JAK_STAT3_SIGNALING"
[25] "HALLMARK_INFLAMMATORY_RESPONSE"
[26] "HALLMARK_INTERFERON_ALPHA_RESPONSE"
[27] "HALLMARK_INTERFERON_GAMMA_RESPONSE"
[28] "HALLMARK_KRAS_SIGNALING_DN"
[29] "HALLMARK_KRAS_SIGNALING_UP"
[30] "HALLMARK_MITOTIC_SPINDLE"
[31] "HALLMARK_MTORC1_SIGNALING"
[32] "HALLMARK_MYC_TARGETS_V1"
[33] "HALLMARK_MYC_TARGETS_V2"
[34] "HALLMARK_MYOGENESIS"
[35] "HALLMARK_NOTCH_SIGNALING"
[36] "HALLMARK_OXIDATIVE_PHOSPHORYLATION"
[37] "HALLMARK_P53_PATHWAY"
[38] "HALLMARK_PANCREAS_BETA_CELLS"
[39] "HALLMARK_PEROXISOME"
[40] "HALLMARK_PI3K_AKT_MTOR_SIGNALING"
[41] "HALLMARK_PROTEIN_SECRETION"
[42] "HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY"
[43] "HALLMARK_SPERMATOGENESIS"
[44] "HALLMARK_TGF_BETA_SIGNALING"
[45] "HALLMARK_TNFA_SIGNALING_VIA_NFKB"
[46] "HALLMARK_UNFOLDED_PROTEIN_RESPONSE"
[47] "HALLMARK_UV_RESPONSE_DN"
[48] "HALLMARK_UV_RESPONSE_UP"
[49] "HALLMARK_WNT_BETA_CATENIN_SIGNALING"
[50] "HALLMARK_XENOBIOTIC_METABOLISM"
# Pathway names
pathway_names <- c(colnames(immune_seurat@meta.data)[grep("REACTOME", colnames(immune_seurat@meta.data))],
colnames(immune_seurat@meta.data)[grep("KEGG", colnames(immune_seurat@meta.data))],
colnames(immune_seurat@meta.data)[grep("BIOCARTA", colnames(immune_seurat@meta.data))],
colnames(immune_seurat@meta.data)[grep("HALLMARK", colnames(immune_seurat@meta.data))])
table(sapply(strsplit(pathway_names, split='_', fixed=TRUE), `[`, 1))
BIOCARTA HALLMARK KEGG REACTOME
291 50 186 1602
# Plotting results for all immune cells
gsea_res <- immune_seurat@meta.data %>%
dplyr::select(all_of(c("TGFbELISA2", pathway_names)))
gsea_res <- gsea_res %>% filter(TGFbELISA2 %in% c("HighTGFb", "LowTGFb"))
output_immune <- data.frame(getSignificance(gsea_res, group = "TGFbELISA2", fit = "Wilcoxon"))
output_immune$pathways <- rownames(output_immune)
output_immune <- output_immune %>% filter(FDR < 0.01) %>% arrange(FDR)
VlnPlot(immune_seurat,
group.by = "TGFbELISA2",
features = c("REACTOME_SIGNALING_BY_TGFB_FAMILY_MEMBERS"),
pt.size = 0)
Plotting TGFb pathways in all immune cells
# Plotting TGFb pathways
output_immune_tgf <- output_immune %>% filter(pathways %in% output_immune$pathways[grep("TGF", output_immune$pathways)])
output_immune_tgf %>%
mutate(delta = median.HighTGFb - median.LowTGFb,
sign = sign(delta),
signstr = if_else(sign == 1, "HighTGFb", "LowTGFb")) %>%
ggplot(aes(x = delta, y = reorder(pathways, delta), fill = signstr)) +
geom_bar(stat = "identity") +
#geom_col(width = 0.85) +
scale_fill_manual(values = c("orangered1", "royalblue3")) +
theme_classic() +
manuscript_theme +
ylab("") +
xlab(expression(Delta ~ "median enrichment score"))
#coord_flip()
# Selecting a subset of pathways for plotting
output_immune_plot <- output_immune %>% head(n=20)
output_immune_plot %>%
mutate(delta = median.HighTGFb - median.LowTGFb,
sign = sign(delta),
signstr = if_else(sign == 1, "HighTGFb", "LowTGFb")) %>%
ggplot(aes(x = delta, y = reorder(pathways, delta), fill = signstr)) +
geom_bar(stat = "identity") +
#geom_col(width = 0.85) +
scale_fill_manual(values = c("orangered1", "royalblue3")) +
theme_classic() +
manuscript_theme +
ylab("") +
xlab(expression(Delta ~ "median enrichment score"))
#coord_flip()
write.table(output_immune, "/scratch/hnatri/CART/TGFb_high_vs_low_GSEA_sig.tsv",
quote = F, row.names = F, sep = "\t")
Myeloid only GSEA
# Myeloid celltypes only
myeloid <- subset(immune_seurat, subset = celltype %in% c(paste0("M", seq(1, 9)), "B1", "N1"))
gsea_res_myeloid <- myeloid@meta.data %>%
dplyr::select(c("orig.ident", "TGFbELISA2", pathway_names))
gsea_res_myeloid <- gsea_res_myeloid %>% filter(TGFbELISA2 %in% c("HighTGFb", "LowTGFb"))
output_myeloid <- data.frame(getSignificance(gsea_res_myeloid, group = "TGFbELISA2", fit = "Wilcoxon"))
output_myeloid$pathways <- rownames(output_myeloid)
output_myeloid <- output_myeloid %>% filter(FDR < 0.01) %>% arrange(FDR)
write.table(output_myeloid, "/scratch/hnatri/CART/TGFb_high_vs_low_myeloid_GSEA_sig.tsv",
quote = F, row.names = F, sep = "\t")
output_myeloid$pathways[grep("INTERFERON", output_myeloid$pathways)]
[1] "REACTOME_INTERFERON_GAMMA_SIGNALING"
[2] "REACTOME_INTERFERON_SIGNALING"
[3] "REACTOME_INTERFERON_ALPHA_BETA_SIGNALING"
[4] "REACTOME_DDX58_IFIH1_MEDIATED_INDUCTION_OF_INTERFERON_ALPHA_BETA"
TGFbhigh_enrichment <- myeloid@meta.data %>% filter(TGFbELISA2 == "HighTGFb") %>%
dplyr::select(REACTOME_INTERFERON_GAMMA_SIGNALING) %>% unlist() %>% as.numeric()
TGFblow_enrichment <- myeloid@meta.data %>% filter(TGFbELISA2 == "LowTGFb") %>%
dplyr::select(REACTOME_INTERFERON_GAMMA_SIGNALING) %>% unlist() %>% as.numeric()
summary(TGFbhigh_enrichment)
Min. 1st Qu. Median Mean 3rd Qu. Max.
2365 4199 4597 4602 4987 6855
summary(TGFblow_enrichment)
Min. 1st Qu. Median Mean 3rd Qu. Max.
3069 4620 5069 5062 5516 7241
t.test(TGFbhigh_enrichment, TGFblow_enrichment)
Welch Two Sample t-test
data: TGFbhigh_enrichment and TGFblow_enrichment
t = -28.774, df = 6107, p-value < 2.2e-16
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-490.8579 -428.2405
sample estimates:
mean of x mean of y
4602.029 5061.579
output_myeloid[which(output_myeloid$pathways == "REACTOME_INTERLEUKIN_27_SIGNALING"),]
W.statistic p.value FDR median.LowTGFb median.HighTGFb
31 3189886 6.53562e-101 1.339149e-97 1370.491 2315.554
pathways
31 REACTOME_INTERLEUKIN_27_SIGNALING
output_myeloid[which(output_myeloid$pathways == "REACTOME_INTERFERON_GAMMA_SIGNALING"),]
W.statistic p.value FDR median.LowTGFb median.HighTGFb
2 2794825 2.795529e-161 5.809109e-158 4596.855 5069.075
pathways
2 REACTOME_INTERFERON_GAMMA_SIGNALING
VlnPlot(myeloid,
features = "REACTOME_INTERFERON_GAMMA_SIGNALING",
group.by = "TGFbELISA2",
pt.size = 0)
# Selecting a subset of pathways for plotting
output_plot_myeloid <- output_myeloid %>% head(n=60)
output_plot_myeloid %>%
mutate(delta = median.HighTGFb - median.LowTGFb,
sign = sign(delta),
signstr = if_else(sign == 1, "HighTGFb", "LowTGFb")) %>%
ggplot(aes(x = delta, y = reorder(pathways, delta), fill = signstr)) +
geom_bar(stat = "identity") +
#geom_col(width = 0.85) +
scale_fill_manual(values = c("orangered1", "royalblue3")) +
theme_classic() +
manuscript_theme +
ylab("") +
xlab(expression(Delta ~ "median enrichment score"))
#coord_flip()
Lymphoid only GSEA
sessionInfo()
R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] circlize_0.4.15 plyr_1.8.8
[3] ComplexHeatmap_2.18.0 viridis_0.6.3
[5] viridisLite_0.4.2 RColorBrewer_1.1-3
[7] escape_1.12.0 scProportionTest_0.0.0.9000
[9] patchwork_1.1.2 ggrepel_0.9.3
[11] lubridate_1.9.2 forcats_1.0.0
[13] stringr_1.5.0 purrr_1.0.1
[15] readr_2.1.4 tidyr_1.3.0
[17] tibble_3.2.1 ggplot2_3.4.2
[19] tidyverse_2.0.0 dplyr_1.1.2
[21] googlesheets4_1.1.0 Seurat_5.0.1
[23] SeuratObject_5.0.1 sp_1.6-1
[25] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] fs_1.6.2 matrixStats_1.0.0
[3] GSVA_1.50.5 spatstat.sparse_3.0-1
[5] bitops_1.0-7 doParallel_1.0.17
[7] httr_1.4.6 tools_4.3.0
[9] sctransform_0.4.1 backports_1.4.1
[11] utf8_1.2.3 R6_2.5.1
[13] HDF5Array_1.30.1 lazyeval_0.2.2
[15] uwot_0.1.14 GetoptLong_1.0.5
[17] rhdf5filters_1.14.1 withr_2.5.0
[19] gridExtra_2.3 progressr_0.13.0
[21] cli_3.6.1 Biobase_2.62.0
[23] Cairo_1.6-0 spatstat.explore_3.2-1
[25] fastDummies_1.7.3 labeling_0.4.2
[27] sass_0.4.6 spatstat.data_3.0-1
[29] ggridges_0.5.4 pbapply_1.7-0
[31] parallelly_1.36.0 limma_3.58.1
[33] rstudioapi_0.14 RSQLite_2.3.1
[35] shape_1.4.6 generics_0.1.3
[37] ica_1.0-3 spatstat.random_3.1-5
[39] Matrix_1.6-5 ggbeeswarm_0.7.2
[41] fansi_1.0.4 S4Vectors_0.40.2
[43] abind_1.4-5 lifecycle_1.0.3
[45] whisker_0.4.1 yaml_2.3.7
[47] SummarizedExperiment_1.32.0 rhdf5_2.46.1
[49] SparseArray_1.2.3 Rtsne_0.16
[51] blob_1.2.4 promises_1.2.0.1
[53] crayon_1.5.2 miniUI_0.1.1.1
[55] lattice_0.21-8 beachmat_2.18.1
[57] msigdbr_7.5.1 cowplot_1.1.1
[59] annotate_1.80.0 KEGGREST_1.42.0
[61] magick_2.7.4 pillar_1.9.0
[63] knitr_1.43 GenomicRanges_1.54.1
[65] rjson_0.2.21 future.apply_1.11.0
[67] codetools_0.2-19 leiden_0.4.3
[69] glue_1.6.2 getPass_0.2-4
[71] data.table_1.14.8 vctrs_0.6.2
[73] png_0.1-8 spam_2.9-1
[75] cellranger_1.1.0 gtable_0.3.3
[77] cachem_1.0.8 xfun_0.39
[79] S4Arrays_1.2.0 mime_0.12
[81] survival_3.5-5 gargle_1.4.0
[83] SingleCellExperiment_1.24.0 iterators_1.0.14
[85] statmod_1.5.0 ellipsis_0.3.2
[87] fitdistrplus_1.1-11 ROCR_1.0-11
[89] nlme_3.1-162 bit64_4.0.5
[91] RcppAnnoy_0.0.20 GenomeInfoDb_1.38.5
[93] rprojroot_2.0.3 bslib_0.4.2
[95] irlba_2.3.5.1 vipor_0.4.5
[97] KernSmooth_2.23-21 colorspace_2.1-0
[99] BiocGenerics_0.48.1 DBI_1.1.3
[101] ggrastr_1.0.2 UCell_2.6.2
[103] tidyselect_1.2.0 processx_3.8.1
[105] curl_5.0.0 bit_4.0.5
[107] compiler_4.3.0 git2r_0.32.0
[109] graph_1.80.0 BiocNeighbors_1.20.2
[111] DelayedArray_0.28.0 plotly_4.10.2
[113] scales_1.2.1 lmtest_0.9-40
[115] callr_3.7.3 digest_0.6.31
[117] goftest_1.2-3 presto_1.0.0
[119] spatstat.utils_3.0-3 rmarkdown_2.22
[121] XVector_0.42.0 htmltools_0.5.5
[123] pkgconfig_2.0.3 sparseMatrixStats_1.14.0
[125] MatrixGenerics_1.14.0 highr_0.10
[127] fastmap_1.1.1 GlobalOptions_0.1.2
[129] rlang_1.1.1 htmlwidgets_1.6.2
[131] shiny_1.7.4 DelayedMatrixStats_1.24.0
[133] farver_2.1.1 jquerylib_0.1.4
[135] zoo_1.8-12 jsonlite_1.8.5
[137] BiocParallel_1.36.0 BiocSingular_1.18.0
[139] RCurl_1.98-1.12 magrittr_2.0.3
[141] GenomeInfoDbData_1.2.11 dotCall64_1.0-2
[143] Rhdf5lib_1.24.1 munsell_0.5.0
[145] Rcpp_1.0.10 babelgene_22.9
[147] reticulate_1.29 stringi_1.7.12
[149] zlibbioc_1.48.0 MASS_7.3-60
[151] parallel_4.3.0 listenv_0.9.0
[153] deldir_1.0-9 Biostrings_2.70.1
[155] splines_4.3.0 tensor_1.5
[157] hms_1.1.3 ps_1.7.5
[159] igraph_1.4.3 spatstat.geom_3.2-1
[161] RcppHNSW_0.5.0 reshape2_1.4.4
[163] stats4_4.3.0 ScaledMatrix_1.10.0
[165] XML_3.99-0.14 evaluate_0.21
[167] foreach_1.5.2 tzdb_0.4.0
[169] httpuv_1.6.11 RANN_2.6.1
[171] polyclip_1.10-4 clue_0.3-64
[173] future_1.32.0 scattermore_1.2
[175] rsvd_1.0.5 broom_1.0.4
[177] xtable_1.8-4 RSpectra_0.16-1
[179] later_1.3.1 googledrive_2.1.0
[181] beeswarm_0.4.0 memoise_2.0.1
[183] AnnotationDbi_1.64.1 IRanges_2.36.0
[185] cluster_2.1.4 timechange_0.2.0
[187] globals_0.16.2 GSEABase_1.64.0